title: Enhanced computer vision with Microsoft Kinect sensor: a review.
abstract: With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.
authors.forename: Jungong
authors.initials: J
authors.lastname: Han
authors.forename: Ling
authors.initials: L
authors.lastname: Shao
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.forename: Jamie
authors.initials: J
authors.lastname: Shotton
mesh_terms: D056044:Actigraphy / Q000295:instrumentation / Q000379:methods; D000465:Algorithms; D001185:Artificial Intelligence; D016287:Computer Peripherals; D003198:Computer Simulation; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers; D018910:Video Games; D051598:Whole Body Imaging / Q000295:instrumentation / Q000379:methods*